1. Field of the Invention
The present invention generally relates to extracting information from text. More specifically, in a relational learning system, a pattern learner module receives a small number of learning samples defined by user interactions in relational pattern templates format wherein elements are defined in a precedence relation and in an inclusion relation, and calculates a minimal most specific generalization (MMSG) for these samples so that information matching the generalized template can then be extracted from unseen text.
2. Description of the Related Art
Extracting relational information from text is an important and unsolved problem in the area of Unstructured Information Management. Many applications including search, question answering, and combining unstructured and structured information could benefit from accurate extraction of relational information.
The present invention deals with learning to recognize patterns in text (training data) that characterize the presence of a kind of information in the training data and applying the learned patterns to extract similar kinds of information from new text (unseen text). In particular, the present invention deals with learning to recognize patterns that characterize when a particular relation exists between textual elements, mentions of named entities, or phrases that are present in text. This type of learning from text is sometimes called ‘relational learning’.
Each specific occurrence of a relation is termed a “relation instance”. Often, of particular interest is a relation that describes a kind of event that has occurred, that is occurring or that will occur. In the setting of a relation that describes a kind of event, a relation instance may be termed an “event mention”.
For instance, from a sentence such as “Jack Jones was appointed CEO of XYZ Corp last week”, a relational learning system might extract the relational information: [Relation: Appoint, Appointee: “Jack Jones”, Role: “CEO of XYZ Corp”].
From this example, it can be understood that relational learning involves a defined “relation” that includes one or more “parameters” that fit into the relation “template”. In the example, “Jack Jones” is the “Appointee” and “CEO of XYZ Corp” is the “Role”. “Appointee” and “Role” are the argument names of the “Appoint” relation. It should also be noted that the template implies a relationship between the arguments, such as the order of the argument or the interconnection understood by a word or sentence structure, such as a verb or prepositional phrase relationship. The significance of determining this relationship will become apparent as the present invention is further described, since the inventors have recognized that a mere ordering of tokens is insufficient for effective information extraction.
As an exemplary scenario for which the present invention might be used is one in which a user (exemplarily, a non-specialist) wishes to search a database or perhaps the Internet to find data items that, for example, identify CEOs of corporations.
Basically, there are currently two main approaches for this relational learning problem:                (1) manual development of patterns; and        (2) learning patterns using machine learning techniques.        
Manual approaches are very costly to develop, since they require experts in computational linguistics or related disciplines to develop formal grammars or special purpose programs. Non-specialists cannot customize manual systems for new domains, tasks or languages.
Machine learning approaches fall into two classes:                (i) statistical approaches; and        (ii) symbolic approaches.        
Machine learning approaches have the advantage that they require only labeled examples of the information sought. Statistical methods are quite popular, but they suffer from the problem of labeling sufficient data accurately for training a model. This is a major problem for such approaches.
Moreover, as the relations of interest vary from task to task and even from individual to individual, methods are needed to learn how to extract relations of interest on demand. Further, it would be desirable that non-specialists be able to use the relational learning tool.
There are currently no adequate solutions to the problem of trainable relation extraction systems, especially no adequate systems that can be used by non-specialists.
Thus, a need continues for a method and system that, as relations of interest vary from task to task and even from individual to individual, learn how to extract relations of interest on demand. Further, it would be desirable that non-specialists be easily able to use a relational learning system.